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    Calibration of High Dimensional Compressive Sensing Systems: A Case Study in Compressive Hyperspectral Imaging

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    Author
    Poon, Phillip
    Dunlop, Matthew
    Advisor
    Gehm, Michael
    Affiliation
    University of Arizona
    Issue Date
    2013-10
    Keywords
    Compressive Sensing
    hyperspectral imaging
    calibration
    
    Metadata
    Show full item record
    Rights
    Copyright © held by the author; distribution rights International Foundation for Telemetering
    Collection Information
    Proceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.
    Publisher
    International Foundation for Telemetering
    Journal
    International Telemetering Conference Proceedings
    Abstract
    Compressive Sensing (CS) is a set of techniques that can faithfully acquire a signal from sub- Nyquist measurements, provided the class of signals have certain broadly-applicable properties. Reconstruction (or exploitation) of the signal from these sub-Nyquist measurements requires a forward model - knowledge of how the system maps signals to measurements. In high-dimensional CS systems, determination of this forward model via direct measurement of the system response to the complete set of impulse functions is impractical. In this paper, we will discuss the development of a parameterized forward model for the Adaptive, Feature-Specific Spectral Imaging Classifier (AFSSI-C), an experimental compressive spectral image classifier. This parameterized forward model drastically reduces the number of calibration measurements.
    Sponsors
    International Foundation for Telemetering
    ISSN
    0884-5123
    0074-9079
    Additional Links
    http://www.telemetry.org/
    Collections
    International Telemetering Conference Proceedings, Volume 49 (2013)

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